import os
import random
import numpy as np
import pandas as pd
from skimage import io
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
# Pytorch
import torch, torchvision, torch.utils
from torch import Tensor
from torch import cat
from torch.autograd.grad_mode import no_grad
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torch.optim as optim
from torch.nn import (
Module,
Conv2d,
Linear,
Dropout2d,
NLLLoss, BCELoss, CrossEntropyLoss, MSELoss,
MaxPool2d,
Flatten,
Sequential,
ReLU,
)
import torch.nn.functional as F
from torchviz import make_dot
from torchsummary import summary
from sklearn.metrics import accuracy_score,ConfusionMatrixDisplay,RocCurveDisplay,classification_report,precision_score,recall_score,f1_score
import time
def matrix_confusion(yt, yp,time_taken):
data = {'Y_Real': yt,
'Y_Pred': yp}
df = pd.DataFrame(data, columns=['Y_Real','Y_Pred'])
confusion_matrix = pd.crosstab(df['Y_Real'], df['Y_Pred'], rownames=['Real'], colnames=['Predicted'])
sns.heatmap(confusion_matrix, annot=True, fmt='g')
plt.show()
y_val = df['Y_Real'].to_numpy()
predictions = df['Y_Pred'].to_numpy()
accuracy = accuracy_score(y_val,predictions)
precision = precision_score(y_val,predictions)
recall = recall_score(y_val,predictions)
f1 = f1_score(y_val,predictions)
print('Time taken: ',time_taken)
print('Test size:',len(y_val))
print('Total Accuracy: ',accuracy)
print('Total Precision: ',precision)
print('Total Recall: ',recall)
print('Total F1 Score: ',f1)
print("Classification Report:\n")
print(classification_report(y_val,predictions))
return time_taken,accuracy,precision,recall,f1
torch.cuda.is_available()
True
train_data = torchvision.datasets.ImageFolder('C:/Users/rjuya/OneDrive/Desktop/github stuff/CNLDS2023 Conference/Data/train', transform=transforms.Compose([transforms.ToTensor()]))
test_data = torchvision.datasets.ImageFolder('C:/Users/rjuya/OneDrive/Desktop/github stuff/CNLDS2023 Conference/Data/test', transform=transforms.Compose([transforms.ToTensor()]))
valid_data = torchvision.datasets.ImageFolder('C:/Users/rjuya/OneDrive/Desktop/github stuff/CNLDS2023 Conference/Data/valid', transform=transforms.Compose([transforms.ToTensor()]))
train_data[0][0].shape
torch.Size([3, 640, 640])
train_loader = DataLoader(train_data, shuffle=True, batch_size=8)
test_loader = DataLoader(valid_data, shuffle=True, batch_size=1)
hold_out_test_loader = DataLoader(test_data, shuffle=True, batch_size=1)
# Normal (0) and True, Pneumonia (1)
print((train_loader.dataset.class_to_idx))
print((test_loader.dataset.class_to_idx))
print((hold_out_test_loader.dataset.class_to_idx))
{'NORMAL': 0, 'PNEUMONIA': 1}
{'NORMAL': 0, 'PNEUMONIA': 1}
{'NORMAL': 0, 'PNEUMONIA': 1}
n_samples_show = 6
data_iter = iter(train_loader)
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 10))
while n_samples_show > 0:
images, targets = data_iter.__next__()
axes[n_samples_show - 1].imshow(images[0, 0].numpy().squeeze(), cmap=plt.cm.rainbow)
axes[n_samples_show - 1].set_xticks([])
axes[n_samples_show - 1].set_yticks([])
axes[n_samples_show - 1].set_title(f"Labeled: {targets[0].item()}")
n_samples_show -= 1
class LogisticRegression(Module):
def __init__(self,input_size,num_classes):
super(LogisticRegression,self).__init__()
self.linear = Linear(input_size,num_classes)
def forward(self,x):
x = x.view(x.size(0), -1)
return torch.sigmoid(self.linear(x))
input_size = 640*640*3 #Size of image
num_classes = 2 #the image number are in range 0-10
epochs = 4 #one cycle through the full train data
# batch_size = 100 #sample size consider before updating the model’s weights
learning_rate = 0.001 #step size to update parameter
model = LogisticRegression(input_size,num_classes)
loss_func = CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=learning_rate)
tic = time.time()
loss_list = []
total_accuracy = []
for epoch in range(epochs):
correct = 0
total_loss = []
batch_size = 0
for i, (images, labels) in enumerate(train_loader):
batch_size+=1
optimizer.zero_grad()
# print(images.shape)
# print(images.view(-1, input_size).shape)
output = model(images)
loss = loss_func(output, labels)
# Loss.append(loss.item())
loss.backward()
optimizer.step()
total_loss.append(loss.item())
train_pred = output.argmax(dim=1, keepdim=True)
correct += train_pred.eq(labels.view_as(train_pred)).sum().item()
loss_list.append(sum(total_loss) / len(total_loss))
accuracy = 100 * correct / 4077 # No. of training examples = 4077
total_accuracy.append(accuracy)
print(f"Training [{100.0 * (epoch + 1) / epochs:.0f}%]\tLoss: {loss_list[-1]:.4f}\tAccuracy: {accuracy:.2f}%")
toc = time.time()
time_taken = toc-tic
print('Time taken: ',time_taken)
Training [25%] Loss: 0.5845 Accuracy: 72.82% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [75%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 85.73757076263428
fig, ax1 = plt.subplots()
ax1.plot(loss_list, 'g-')
ax2 = ax1.twinx()
ax2.plot(total_accuracy, 'b')
plt.title("Logistic Regression Training Convergence", color='red')
ax1.set_xlabel("Training Iterations")
ax1.set_ylabel("Cross Entropy Loss", color='g')
ax2.set_ylabel("Accuracy (%)", color='b')
plt.show()
model_path = 'C:/Users/rjuya/OneDrive/Desktop/github stuff/EE5610/Project/Models/log_reg model.pt'
torch.save(model.state_dict(), model_path)
model_temp = LogisticRegression(input_size,num_classes)
model_temp.load_state_dict(torch.load(model_path))
<All keys matched successfully>
batch_size=1
model_temp.eval()
pred_targets = []
test_targets= []
with no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
output = model_temp(data)
if len(output.shape) == 1:
output = output.reshape(1, *output.shape)
pred = output.argmax(dim=1, keepdim=True)
pred_targets.append(pred.item())
test_targets.append(target.item())
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print(f"Performance on test data:\n\tLoss: {sum(total_loss) / len(total_loss):.4f}\n\tAccuracy: {100 * correct / len(test_loader) / batch_size:.2f}%")
print(test_targets)
print(pred_targets)
Performance on test data: Loss: 0.5772 Accuracy: 73.91% [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
from PIL import Image
#abc
n_samples_show = 6
count = 0
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))
model_temp.eval()
with no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
if count == n_samples_show:
break
output = model_temp(data[0:1])
if len(output.shape) == 1:
output = output.reshape(3, *output.shape)
pred = output.argmax(dim=1, keepdim=True)
test_targets2 = target.item()
axes[count].imshow(torchvision.transforms.ToPILImage(mode='RGB')(data[0].squeeze()), cmap=plt.cm.rainbow)
axes[count].set_xticks([])
axes[count].set_yticks([])
axes[count].set_title("Predicted {} \n Actual {}".format(pred.item(),test_targets2))
count += 1
val_logreg = matrix_confusion(test_targets, pred_targets,time_taken)
Time taken: 85.73757076263428
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
out = pd.DataFrame([val_logreg],
index = ['Logistic Regression'],
columns=['Time','Accuracy','Precision','Recall','F1 score'])
out.sort_values('Time')
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression | 85.737571 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
import pickle
from IPython.display import display
batch_sizes = [16, 32, 64]
num_epochs_list = [10,20,30]
learning_rates = [0.0001,0.001, 0.01, 0.1,1]
optimizers = ['SGD','Adam']
input_size = 640*640*3 #Size of image
num_classes = 2 #the image number are in range 0-10
number_done = 0
for size in batch_sizes:
train_loader = DataLoader(train_data, shuffle=True, batch_size=size)
test_loader = DataLoader(valid_data, shuffle=True, batch_size=1)
for epochs in num_epochs_list:
for learning_rate in learning_rates:
for optimizer_name in optimizers:
print(f"Training with batch size: {size}, epochs: {epochs}, learning rate: {learning_rate}, optimizer: {optimizer_name}")
number_done+=1
print(f"Current: {number_done}/90")
# Define logistic regression model
model = LogisticRegression(input_size,num_classes)
loss_func = CrossEntropyLoss()
if optimizer_name == 'SGD':
optimizer = optim.SGD(model.parameters(),lr=learning_rate)
else:
optimizer = optim.Adam(model.parameters(),lr=learning_rate)
# Training loop
tic = time.time()
loss_list = []
total_accuracy = []
for epoch in range(epochs):
correct = 0
total_loss = []
batch_size = 0
for i, (images, labels) in enumerate(train_loader):
batch_size+=1
optimizer.zero_grad()
# print(images.shape)
# print(images.view(-1, input_size).shape)
output = model(images)
loss = loss_func(output, labels)
# Loss.append(loss.item())
loss.backward()
optimizer.step()
total_loss.append(loss.item())
train_pred = output.argmax(dim=1, keepdim=True)
correct += train_pred.eq(labels.view_as(train_pred)).sum().item()
loss_list.append(sum(total_loss) / len(total_loss))
accuracy = 100 * correct / 4077 # No. of training examples = 4077
total_accuracy.append(accuracy)
print(f"Training [{100.0 * (epoch + 1) / epochs:.0f}%]\tLoss: {loss_list[-1]:.4f}\tAccuracy: {accuracy:.2f}%")
# Evaluate the model on the test set
toc = time.time()
time_taken = toc-tic
print('Time taken: ',time_taken)
fig, ax1 = plt.subplots()
ax1.plot(loss_list, 'g-')
ax2 = ax1.twinx()
ax2.plot(total_accuracy, 'b')
plt.title("Logistic Regression Training Convergence", color='red')
ax1.set_xlabel("Training Iterations")
ax1.set_ylabel("Cross Entropy Loss", color='g')
ax2.set_ylabel("Accuracy (%)", color='b')
plt.show()
model_path = f"C:/Users/rjuya/OneDrive/Desktop/github stuff/EE5610/Project/Models/log_reg model_batch{size}_epochs{epochs}_lr{learning_rate}_optimizer{optimizer_name}.pt"
torch.save(model.state_dict(), model_path)
model_temp = LogisticRegression(input_size,num_classes)
model_temp.load_state_dict(torch.load(model_path))
batch_size=1
model_temp.eval()
pred_targets = []
test_targets= []
with no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
output = model_temp(data)
if len(output.shape) == 1:
output = output.reshape(1, *output.shape)
pred = output.argmax(dim=1, keepdim=True)
pred_targets.append(pred.item())
test_targets.append(target.item())
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print(f"Performance on test data:\n\tLoss: {sum(total_loss) / len(total_loss):.4f}\n\tAccuracy: {100 * correct / len(test_loader) / batch_size:.2f}%")
from PIL import Image
#abc
n_samples_show = 6
count = 0
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))
model_temp.eval()
with no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
if count == n_samples_show:
break
output = model_temp(data[0:1])
if len(output.shape) == 1:
output = output.reshape(3, *output.shape)
pred = output.argmax(dim=1, keepdim=True)
test_targets2 = target.item()
axes[count].imshow(torchvision.transforms.ToPILImage(mode='RGB')(data[0].squeeze()), cmap=plt.cm.rainbow)
axes[count].set_xticks([])
axes[count].set_yticks([])
axes[count].set_title("Predicted {} \n Actual {}".format(pred.item(),test_targets2))
count += 1
plt.show()
name_logreg = f"Logistic Regression model_batch{size}_epochs{epochs}_lr{learning_rate}_optimizer{optimizer_name}"
val_logreg = matrix_confusion(test_targets, pred_targets,time_taken)
out = pd.DataFrame([val_logreg],
index = [name_logreg],
columns=['Time','Accuracy','Precision','Recall','F1 score'])
out.sort_values('Time')
display(out)
filepath1 = f"C:/Users/rjuya/OneDrive/Desktop/github stuff/EE5610/Project/Models/output of log_reg model_batch{size}_epochs{epochs}_lr{learning_rate}_optimizer{optimizer_name}.pickle"
with open(filepath1, 'wb') as file:
# Serialize and write the variable to the file
pickle.dump(out, file)
print("Data Stored Successfully")
Training with batch size: 16, epochs: 10, learning rate: 0.0001, optimizer: SGD Current: 1/90 Training [10%] Loss: 0.5672 Accuracy: 72.73% Training [20%] Loss: 0.5082 Accuracy: 72.95% Training [30%] Loss: 0.4885 Accuracy: 72.92% Training [40%] Loss: 0.4804 Accuracy: 72.99% Training [50%] Loss: 0.4753 Accuracy: 73.07% Training [60%] Loss: 0.4720 Accuracy: 73.09% Training [70%] Loss: 0.4690 Accuracy: 73.14% Training [80%] Loss: 0.4675 Accuracy: 73.19% Training [90%] Loss: 0.4655 Accuracy: 73.17% Training [100%] Loss: 0.4639 Accuracy: 73.26% Time taken: 189.16188311576843
Performance on test data: Loss: 0.4564 Accuracy: 74.25%
Time taken: 189.16188311576843
Test size: 1165
Total Accuracy: 0.7424892703862661
Total Precision: 0.7416020671834626
Total Recall: 1.0
Total F1 Score: 0.8516320474777448
Classification Report:
precision recall f1-score support
0 1.00 0.01 0.03 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.87 0.51 0.44 1165
weighted avg 0.81 0.74 0.64 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.0001_optimizerSGD | 189.161883 | 0.742489 | 0.741602 | 1.0 | 0.851632 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.0001, optimizer: Adam Current: 2/90 Training [10%] Loss: 0.5848 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 230.20081782341003
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 230.20081782341003
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.0001_optimizerAdam | 230.200818 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.001, optimizer: SGD Current: 3/90 Training [10%] Loss: 0.5844 Accuracy: 72.82% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 193.41015458106995
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 193.41015458106995
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.001_optimizerSGD | 193.410155 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.001, optimizer: Adam Current: 4/90 Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 228.5272650718689
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 228.5272650718689
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.001_optimizerAdam | 228.527265 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.01, optimizer: SGD Current: 5/90 Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 198.85651850700378
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 198.85651850700378
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.01_optimizerSGD | 198.856519 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.01, optimizer: Adam Current: 6/90 Training [10%] Loss: 0.5854 Accuracy: 72.63% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 228.9003622531891
Performance on test data: Loss: 0.5759 Accuracy: 73.91%
Time taken: 228.9003622531891
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.01_optimizerAdam | 228.900362 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.1, optimizer: SGD Current: 7/90 Training [10%] Loss: 0.5846 Accuracy: 72.85% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 192.21956396102905
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 192.21956396102905
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.1_optimizerSGD | 192.219564 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 0.1, optimizer: Adam Current: 8/90 Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 229.27179980278015
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 229.27179980278015
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr0.1_optimizerAdam | 229.2718 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 1, optimizer: SGD Current: 9/90 Training [10%] Loss: 0.5847 Accuracy: 72.73% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 193.82004833221436
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 193.82004833221436
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr1_optimizerSGD | 193.820048 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 10, learning rate: 1, optimizer: Adam Current: 10/90 Training [10%] Loss: 0.5851 Accuracy: 72.68% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 235.94986009597778
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 235.94986009597778
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs10_lr1_optimizerAdam | 235.94986 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.0001, optimizer: SGD Current: 11/90 Training [5%] Loss: 0.5529 Accuracy: 72.63% Training [10%] Loss: 0.5013 Accuracy: 73.02% Training [15%] Loss: 0.4864 Accuracy: 73.29% Training [20%] Loss: 0.4792 Accuracy: 73.44% Training [25%] Loss: 0.4740 Accuracy: 73.53% Training [30%] Loss: 0.4715 Accuracy: 73.76% Training [35%] Loss: 0.4687 Accuracy: 73.76% Training [40%] Loss: 0.4666 Accuracy: 73.83% Training [45%] Loss: 0.4651 Accuracy: 74.03% Training [50%] Loss: 0.4641 Accuracy: 73.88% Training [55%] Loss: 0.4629 Accuracy: 74.20% Training [60%] Loss: 0.4620 Accuracy: 74.22% Training [65%] Loss: 0.4610 Accuracy: 74.27% Training [70%] Loss: 0.4600 Accuracy: 74.39% Training [75%] Loss: 0.4595 Accuracy: 74.32% Training [80%] Loss: 0.4588 Accuracy: 74.54% Training [85%] Loss: 0.4585 Accuracy: 74.54% Training [90%] Loss: 0.4582 Accuracy: 74.54% Training [95%] Loss: 0.4577 Accuracy: 74.66% Training [100%] Loss: 0.4571 Accuracy: 74.79% Time taken: 389.0391774177551
Performance on test data: Loss: 0.4499 Accuracy: 75.79%
Time taken: 389.0391774177551
Test size: 1165
Total Accuracy: 0.7579399141630901
Total Precision: 0.7532808398950132
Total Recall: 1.0
Total F1 Score: 0.8592814371257484
Classification Report:
precision recall f1-score support
0 1.00 0.07 0.13 304
1 0.75 1.00 0.86 861
accuracy 0.76 1165
macro avg 0.88 0.54 0.50 1165
weighted avg 0.82 0.76 0.67 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.0001_optimizerSGD | 389.039177 | 0.75794 | 0.753281 | 1.0 | 0.859281 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.0001, optimizer: Adam Current: 12/90 Training [5%] Loss: 0.5843 Accuracy: 72.92% Training [10%] Loss: 0.5840 Accuracy: 72.92% Training [15%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [35%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [85%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [95%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 470.34647822380066
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 470.34647822380066
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.0001_optimizerAdam | 470.346478 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.001, optimizer: SGD Current: 13/90 Training [5%] Loss: 0.5852 Accuracy: 72.70% Training [10%] Loss: 0.5838 Accuracy: 72.92% Training [15%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [25%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [35%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [45%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [85%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [95%] Loss: 0.5843 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 387.1545670032501
Performance on test data: Loss: 0.5759 Accuracy: 73.91%
Time taken: 387.1545670032501
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.001_optimizerSGD | 387.154567 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.001, optimizer: Adam Current: 14/90 Training [5%] Loss: 0.5845 Accuracy: 72.92% Training [10%] Loss: 0.5840 Accuracy: 72.92% Training [15%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5838 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5843 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [85%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [95%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 469.4729800224304
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 469.4729800224304
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.001_optimizerAdam | 469.47298 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.01, optimizer: SGD Current: 15/90 Training [5%] Loss: 0.5848 Accuracy: 72.80% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5838 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [65%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [75%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [95%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 385.9541566371918
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 385.9541566371918
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.01_optimizerSGD | 385.954157 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.01, optimizer: Adam Current: 16/90 Training [5%] Loss: 0.5849 Accuracy: 72.92% Training [10%] Loss: 0.5839 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [25%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [35%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [85%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [95%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 474.3691177368164
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 474.3691177368164
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.01_optimizerAdam | 474.369118 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.1, optimizer: SGD Current: 17/90 Training [5%] Loss: 0.5844 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [15%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [35%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [45%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [65%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [75%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [85%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [95%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 393.33760929107666
Performance on test data: Loss: 0.5759 Accuracy: 73.91%
Time taken: 393.33760929107666
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.1_optimizerSGD | 393.337609 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 0.1, optimizer: Adam Current: 18/90 Training [5%] Loss: 0.5843 Accuracy: 72.77% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [15%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [85%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [95%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 471.4960799217224
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 471.4960799217224
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr0.1_optimizerAdam | 471.49608 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 1, optimizer: SGD Current: 19/90 Training [5%] Loss: 0.5848 Accuracy: 72.70% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [15%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [65%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [85%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [95%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 385.3488972187042
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 385.3488972187042
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr1_optimizerSGD | 385.348897 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 20, learning rate: 1, optimizer: Adam Current: 20/90 Training [5%] Loss: 1.0418 Accuracy: 27.13% Training [10%] Loss: 1.0425 Accuracy: 27.08% Training [15%] Loss: 1.0422 Accuracy: 27.08% Training [20%] Loss: 1.0422 Accuracy: 27.08% Training [25%] Loss: 1.0424 Accuracy: 27.08% Training [30%] Loss: 1.0424 Accuracy: 27.08% Training [35%] Loss: 1.0424 Accuracy: 27.08% Training [40%] Loss: 1.0423 Accuracy: 27.08% Training [45%] Loss: 1.0424 Accuracy: 27.08% Training [50%] Loss: 1.0422 Accuracy: 27.08% Training [55%] Loss: 1.0423 Accuracy: 27.08% Training [60%] Loss: 1.0424 Accuracy: 27.08% Training [65%] Loss: 1.0425 Accuracy: 27.08% Training [70%] Loss: 1.0424 Accuracy: 27.08% Training [75%] Loss: 1.0425 Accuracy: 27.08% Training [80%] Loss: 1.0422 Accuracy: 27.08% Training [85%] Loss: 1.0423 Accuracy: 27.08% Training [90%] Loss: 1.0422 Accuracy: 27.08% Training [95%] Loss: 1.0422 Accuracy: 27.08% Training [100%] Loss: 1.0425 Accuracy: 27.08% Time taken: 485.68366956710815
Performance on test data: Loss: 1.0505 Accuracy: 26.09%
Time taken: 485.68366956710815
Test size: 1165
Total Accuracy: 0.2609442060085837
Total Precision: 0.0
Total Recall: 0.0
Total F1 Score: 0.0
Classification Report:
precision recall f1-score support
0 0.26 1.00 0.41 304
1 0.00 0.00 0.00 861
accuracy 0.26 1165
macro avg 0.13 0.50 0.21 1165
weighted avg 0.07 0.26 0.11 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs20_lr1_optimizerAdam | 485.68367 | 0.260944 | 0.0 | 0.0 | 0.0 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.0001, optimizer: SGD Current: 21/90 Training [3%] Loss: 0.5548 Accuracy: 74.81% Training [7%] Loss: 0.4780 Accuracy: 86.02% Training [10%] Loss: 0.4437 Accuracy: 89.21% Training [13%] Loss: 0.4287 Accuracy: 90.56% Training [17%] Loss: 0.4198 Accuracy: 91.32% Training [20%] Loss: 0.4135 Accuracy: 91.69% Training [23%] Loss: 0.4094 Accuracy: 92.03% Training [27%] Loss: 0.4052 Accuracy: 92.13% Training [30%] Loss: 0.4032 Accuracy: 92.42% Training [33%] Loss: 0.3998 Accuracy: 92.54% Training [37%] Loss: 0.3981 Accuracy: 92.74% Training [40%] Loss: 0.3963 Accuracy: 93.03% Training [43%] Loss: 0.3950 Accuracy: 93.06% Training [47%] Loss: 0.3926 Accuracy: 93.33% Training [50%] Loss: 0.3921 Accuracy: 93.11% Training [53%] Loss: 0.3901 Accuracy: 93.35% Training [57%] Loss: 0.3895 Accuracy: 93.11% Training [60%] Loss: 0.3881 Accuracy: 93.40% Training [63%] Loss: 0.3885 Accuracy: 93.28% Training [67%] Loss: 0.3862 Accuracy: 93.55% Training [70%] Loss: 0.3864 Accuracy: 93.30% Training [73%] Loss: 0.3860 Accuracy: 93.48% Training [77%] Loss: 0.3857 Accuracy: 93.60% Training [80%] Loss: 0.3844 Accuracy: 93.82% Training [83%] Loss: 0.3839 Accuracy: 93.67% Training [87%] Loss: 0.3836 Accuracy: 93.62% Training [90%] Loss: 0.3829 Accuracy: 93.77% Training [93%] Loss: 0.3828 Accuracy: 93.62% Training [97%] Loss: 0.3814 Accuracy: 93.94% Training [100%] Loss: 0.3816 Accuracy: 93.94% Time taken: 582.5460975170135
Performance on test data: Loss: 0.3755 Accuracy: 94.51%
Time taken: 582.5460975170135
Test size: 1165
Total Accuracy: 0.9450643776824035
Total Precision: 0.9596309111880046
Total Recall: 0.9663182346109176
Total F1 Score: 0.962962962962963
Classification Report:
precision recall f1-score support
0 0.90 0.88 0.89 304
1 0.96 0.97 0.96 861
accuracy 0.95 1165
macro avg 0.93 0.93 0.93 1165
weighted avg 0.94 0.95 0.94 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.0001_optimizerSGD | 582.546098 | 0.945064 | 0.959631 | 0.966318 | 0.962963 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.0001, optimizer: Adam Current: 22/90 Training [3%] Loss: 0.5842 Accuracy: 72.82% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5840 Accuracy: 72.92% Training [17%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [33%] Loss: 0.5841 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [43%] Loss: 0.5840 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5843 Accuracy: 72.92% Training [53%] Loss: 0.5841 Accuracy: 72.92% Training [57%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [63%] Loss: 0.5840 Accuracy: 72.92% Training [67%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [73%] Loss: 0.5840 Accuracy: 72.92% Training [77%] Loss: 0.5843 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 703.2330949306488
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 703.2330949306488
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.0001_optimizerAdam | 703.233095 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.001, optimizer: SGD Current: 23/90 Training [3%] Loss: 0.5842 Accuracy: 72.92% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [33%] Loss: 0.5841 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [43%] Loss: 0.5839 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [73%] Loss: 0.5840 Accuracy: 72.92% Training [77%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 581.423731803894
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 581.423731803894
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.001_optimizerSGD | 581.423732 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.001, optimizer: Adam Current: 24/90 Training [3%] Loss: 0.5846 Accuracy: 72.87% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5840 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5839 Accuracy: 72.92% Training [27%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [33%] Loss: 0.5839 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [43%] Loss: 0.5840 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [73%] Loss: 0.5840 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5841 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [93%] Loss: 0.5840 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 707.0185761451721
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 707.0185761451721
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.001_optimizerAdam | 707.018576 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.01, optimizer: SGD Current: 25/90 Training [3%] Loss: 0.5876 Accuracy: 72.53% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5842 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5881 Accuracy: 71.28% Training [37%] Loss: 0.6931 Accuracy: 27.27% Training [40%] Loss: 0.6931 Accuracy: 27.27% Training [43%] Loss: 0.6931 Accuracy: 27.27% Training [47%] Loss: 0.6931 Accuracy: 27.27% Training [50%] Loss: 0.6931 Accuracy: 27.27% Training [53%] Loss: 0.6931 Accuracy: 27.27% Training [57%] Loss: 0.6931 Accuracy: 27.27% Training [60%] Loss: 0.6931 Accuracy: 27.27% Training [63%] Loss: 0.6931 Accuracy: 27.27% Training [67%] Loss: 0.6931 Accuracy: 27.27% Training [70%] Loss: 0.6931 Accuracy: 27.27% Training [73%] Loss: 0.6931 Accuracy: 27.27% Training [77%] Loss: 0.6931 Accuracy: 27.27% Training [80%] Loss: 0.6931 Accuracy: 27.27% Training [83%] Loss: 0.6931 Accuracy: 27.27% Training [87%] Loss: 0.6931 Accuracy: 27.27% Training [90%] Loss: 0.6931 Accuracy: 27.27% Training [93%] Loss: 0.6931 Accuracy: 27.27% Training [97%] Loss: 0.6931 Accuracy: 27.27% Training [100%] Loss: 0.6931 Accuracy: 27.27% Time taken: 577.637809753418
Performance on test data: Loss: 0.6931 Accuracy: 26.18%
Time taken: 577.637809753418
Test size: 1165
Total Accuracy: 0.26180257510729615
Total Precision: 1.0
Total Recall: 0.0011614401858304297
Total F1 Score: 0.002320185614849188
Classification Report:
precision recall f1-score support
0 0.26 1.00 0.41 304
1 1.00 0.00 0.00 861
accuracy 0.26 1165
macro avg 0.63 0.50 0.21 1165
weighted avg 0.81 0.26 0.11 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.01_optimizerSGD | 577.63781 | 0.261803 | 1.0 | 0.001161 | 0.00232 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.01, optimizer: Adam Current: 26/90 Training [3%] Loss: 0.5844 Accuracy: 72.77% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5842 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5840 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5838 Accuracy: 72.92% Training [73%] Loss: 0.5840 Accuracy: 72.92% Training [77%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [83%] Loss: 0.5840 Accuracy: 72.92% Training [87%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 707.8796467781067
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 707.8796467781067
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.01_optimizerAdam | 707.879647 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.1, optimizer: SGD Current: 27/90 Training [3%] Loss: 0.5849 Accuracy: 72.92% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5840 Accuracy: 72.92% Training [27%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [33%] Loss: 0.5840 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5841 Accuracy: 72.92% Training [57%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [63%] Loss: 0.5840 Accuracy: 72.92% Training [67%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5838 Accuracy: 72.92% Training [77%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [83%] Loss: 0.5841 Accuracy: 72.92% Training [87%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5840 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 581.8221063613892
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 581.8221063613892
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.1_optimizerSGD | 581.822106 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 0.1, optimizer: Adam Current: 28/90 Training [3%] Loss: 0.5853 Accuracy: 72.68% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5840 Accuracy: 72.92% Training [17%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [33%] Loss: 0.5841 Accuracy: 72.92% Training [37%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [73%] Loss: 0.5839 Accuracy: 72.92% Training [77%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [83%] Loss: 0.5840 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [93%] Loss: 0.5844 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 711.5526232719421
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 711.5526232719421
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr0.1_optimizerAdam | 711.552623 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 1, optimizer: SGD Current: 29/90 Training [3%] Loss: 0.5842 Accuracy: 72.85% Training [7%] Loss: 0.5839 Accuracy: 72.92% Training [10%] Loss: 0.5840 Accuracy: 72.92% Training [13%] Loss: 0.5842 Accuracy: 72.92% Training [17%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [33%] Loss: 0.5842 Accuracy: 72.92% Training [37%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5840 Accuracy: 72.92% Training [47%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [53%] Loss: 0.5841 Accuracy: 72.92% Training [57%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [73%] Loss: 0.5841 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5840 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [93%] Loss: 0.5840 Accuracy: 72.92% Training [97%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 580.5647571086884
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 580.5647571086884
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr1_optimizerSGD | 580.564757 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 16, epochs: 30, learning rate: 1, optimizer: Adam Current: 30/90 Training [3%] Loss: 0.5846 Accuracy: 72.92% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5841 Accuracy: 72.92% Training [13%] Loss: 0.5840 Accuracy: 72.92% Training [17%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [23%] Loss: 0.5839 Accuracy: 72.92% Training [27%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5841 Accuracy: 72.92% Training [37%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5841 Accuracy: 72.92% Training [47%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5840 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [73%] Loss: 0.5841 Accuracy: 72.92% Training [77%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5838 Accuracy: 72.92% Training [87%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5840 Accuracy: 72.92% Training [97%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 739.7941327095032
Performance on test data: Loss: 0.5760 Accuracy: 73.91%
Time taken: 739.7941327095032
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch16_epochs30_lr1_optimizerAdam | 739.794133 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.0001, optimizer: SGD Current: 31/90 Training [10%] Loss: 0.5713 Accuracy: 72.92% Training [20%] Loss: 0.5247 Accuracy: 72.92% Training [30%] Loss: 0.5033 Accuracy: 73.02% Training [40%] Loss: 0.4936 Accuracy: 73.14% Training [50%] Loss: 0.4872 Accuracy: 73.39% Training [60%] Loss: 0.4830 Accuracy: 73.68% Training [70%] Loss: 0.4798 Accuracy: 73.93% Training [80%] Loss: 0.4769 Accuracy: 74.07% Training [90%] Loss: 0.4737 Accuracy: 74.34% Training [100%] Loss: 0.4722 Accuracy: 74.91% Time taken: 191.06178379058838
Performance on test data: Loss: 0.4649 Accuracy: 74.76%
Time taken: 191.06178379058838
Test size: 1165
Total Accuracy: 0.7476394849785408
Total Precision: 0.7454545454545455
Total Recall: 1.0
Total F1 Score: 0.8541666666666666
Classification Report:
precision recall f1-score support
0 1.00 0.03 0.06 304
1 0.75 1.00 0.85 861
accuracy 0.75 1165
macro avg 0.87 0.52 0.46 1165
weighted avg 0.81 0.75 0.65 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.0001_optimizerSGD | 191.061784 | 0.747639 | 0.745455 | 1.0 | 0.854167 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.0001, optimizer: Adam Current: 32/90 Training [10%] Loss: 0.5854 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5846 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 202.72056889533997
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 202.72056889533997
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.0001_optimizerAdam | 202.720569 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.001, optimizer: SGD Current: 33/90 Training [10%] Loss: 0.5839 Accuracy: 72.75% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5850 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5846 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 189.32573461532593
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 189.32573461532593
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.001_optimizerSGD | 189.325735 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.001, optimizer: Adam Current: 34/90 Training [10%] Loss: 1.0418 Accuracy: 27.03% Training [20%] Loss: 1.0430 Accuracy: 27.08% Training [30%] Loss: 1.0423 Accuracy: 27.08% Training [40%] Loss: 1.0419 Accuracy: 27.08% Training [50%] Loss: 1.0430 Accuracy: 27.08% Training [60%] Loss: 1.0423 Accuracy: 27.08% Training [70%] Loss: 1.0427 Accuracy: 27.08% Training [80%] Loss: 1.0427 Accuracy: 27.08% Training [90%] Loss: 1.0419 Accuracy: 27.08% Training [100%] Loss: 1.0434 Accuracy: 27.08% Time taken: 202.09978866577148
Performance on test data: Loss: 1.0514 Accuracy: 26.09%
Time taken: 202.09978866577148
Test size: 1165
Total Accuracy: 0.2609442060085837
Total Precision: 0.0
Total Recall: 0.0
Total F1 Score: 0.0
Classification Report:
precision recall f1-score support
0 0.26 1.00 0.41 304
1 0.00 0.00 0.00 861
accuracy 0.26 1165
macro avg 0.13 0.50 0.21 1165
weighted avg 0.07 0.26 0.11 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.001_optimizerAdam | 202.099789 | 0.260944 | 0.0 | 0.0 | 0.0 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.01, optimizer: SGD Current: 35/90 Training [10%] Loss: 0.5855 Accuracy: 72.41% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5846 Accuracy: 72.92% Training [40%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5831 Accuracy: 72.92% Training [80%] Loss: 0.5831 Accuracy: 72.92% Training [90%] Loss: 0.5846 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 189.96638703346252
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 189.96638703346252
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.01_optimizerSGD | 189.966387 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.01, optimizer: Adam Current: 36/90 Training [10%] Loss: 0.5842 Accuracy: 72.63% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5853 Accuracy: 72.92% Training [50%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5846 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 200.52976965904236
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 200.52976965904236
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.01_optimizerAdam | 200.52977 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.1, optimizer: SGD Current: 37/90 Training [10%] Loss: 0.5860 Accuracy: 72.33% Training [20%] Loss: 0.5831 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [50%] Loss: 0.5831 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5828 Accuracy: 72.92% Time taken: 189.1295518875122
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 189.1295518875122
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.1_optimizerSGD | 189.129552 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 0.1, optimizer: Adam Current: 38/90 Training [10%] Loss: 0.5853 Accuracy: 72.43% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [30%] Loss: 0.5849 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [50%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5849 Accuracy: 72.92% Time taken: 201.74094033241272
Performance on test data: Loss: 0.5753 Accuracy: 73.91%
Time taken: 201.74094033241272
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr0.1_optimizerAdam | 201.74094 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 1, optimizer: SGD Current: 39/90 Training [10%] Loss: 0.5852 Accuracy: 72.90% Training [20%] Loss: 0.5835 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5849 Accuracy: 72.92% Time taken: 190.5561785697937
Performance on test data: Loss: 0.5753 Accuracy: 73.91%
Time taken: 190.5561785697937
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr1_optimizerSGD | 190.556179 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 10, learning rate: 1, optimizer: Adam Current: 40/90 Training [10%] Loss: 0.5831 Accuracy: 73.02% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5849 Accuracy: 72.92% Training [40%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5828 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 201.5389883518219
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 201.5389883518219
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs10_lr1_optimizerAdam | 201.538988 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.0001, optimizer: SGD Current: 41/90 Training [5%] Loss: 0.5756 Accuracy: 72.53% Training [10%] Loss: 0.5113 Accuracy: 81.09% Training [15%] Loss: 0.4733 Accuracy: 86.68% Training [20%] Loss: 0.4537 Accuracy: 88.62% Training [25%] Loss: 0.4412 Accuracy: 89.82% Training [30%] Loss: 0.4330 Accuracy: 90.58% Training [35%] Loss: 0.4265 Accuracy: 91.24% Training [40%] Loss: 0.4219 Accuracy: 91.46% Training [45%] Loss: 0.4180 Accuracy: 91.54% Training [50%] Loss: 0.4153 Accuracy: 91.76% Training [55%] Loss: 0.4127 Accuracy: 92.08% Training [60%] Loss: 0.4096 Accuracy: 92.15% Training [65%] Loss: 0.4075 Accuracy: 92.37% Training [70%] Loss: 0.4061 Accuracy: 92.47% Training [75%] Loss: 0.4033 Accuracy: 92.84% Training [80%] Loss: 0.4024 Accuracy: 92.81% Training [85%] Loss: 0.4017 Accuracy: 92.52% Training [90%] Loss: 0.4003 Accuracy: 92.69% Training [95%] Loss: 0.3985 Accuracy: 93.16% Training [100%] Loss: 0.3978 Accuracy: 92.94% Time taken: 378.21263670921326
Performance on test data: Loss: 0.3935 Accuracy: 93.73%
Time taken: 378.21263670921326
Test size: 1165
Total Accuracy: 0.9373390557939915
Total Precision: 0.9635294117647059
Total Recall: 0.9512195121951219
Total F1 Score: 0.9573348918760959
Classification Report:
precision recall f1-score support
0 0.87 0.90 0.88 304
1 0.96 0.95 0.96 861
accuracy 0.94 1165
macro avg 0.92 0.92 0.92 1165
weighted avg 0.94 0.94 0.94 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.0001_optimizerSGD | 378.212637 | 0.937339 | 0.963529 | 0.95122 | 0.957335 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.0001, optimizer: Adam Current: 42/90 Training [5%] Loss: 0.5851 Accuracy: 72.92% Training [10%] Loss: 0.5846 Accuracy: 72.92% Training [15%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [25%] Loss: 0.5846 Accuracy: 72.92% Training [30%] Loss: 0.5849 Accuracy: 72.92% Training [35%] Loss: 0.5846 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [55%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5831 Accuracy: 72.92% Training [65%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5849 Accuracy: 72.92% Training [75%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [95%] Loss: 0.5831 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 412.4959604740143
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 412.4959604740143
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.0001_optimizerAdam | 412.49596 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.001, optimizer: SGD Current: 43/90 Training [5%] Loss: 0.5859 Accuracy: 72.92% Training [10%] Loss: 0.5831 Accuracy: 72.92% Training [15%] Loss: 0.5846 Accuracy: 72.92% Training [20%] Loss: 0.5853 Accuracy: 72.92% Training [25%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [35%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5831 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [55%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5846 Accuracy: 72.92% Training [65%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [75%] Loss: 0.5835 Accuracy: 72.92% Training [80%] Loss: 0.5849 Accuracy: 72.92% Training [85%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5831 Accuracy: 72.92% Time taken: 379.46731305122375
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 379.46731305122375
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.001_optimizerSGD | 379.467313 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.001, optimizer: Adam Current: 44/90 Training [5%] Loss: 0.5852 Accuracy: 72.92% Training [10%] Loss: 0.5846 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [25%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5849 Accuracy: 72.92% Training [35%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [45%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5849 Accuracy: 72.92% Training [55%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [65%] Loss: 0.5831 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [75%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [85%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [95%] Loss: 0.5846 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 413.17325258255005
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 413.17325258255005
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.001_optimizerAdam | 413.173253 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.01, optimizer: SGD Current: 45/90 Training [5%] Loss: 0.5843 Accuracy: 72.75% Training [10%] Loss: 0.5835 Accuracy: 72.92% Training [15%] Loss: 0.5831 Accuracy: 72.92% Training [20%] Loss: 0.5849 Accuracy: 72.92% Training [25%] Loss: 0.5846 Accuracy: 72.92% Training [30%] Loss: 0.5846 Accuracy: 72.92% Training [35%] Loss: 0.5856 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [45%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5849 Accuracy: 72.92% Training [65%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5853 Accuracy: 72.92% Training [75%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [85%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 392.598112821579
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 392.598112821579
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.01_optimizerSGD | 392.598113 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.01, optimizer: Adam Current: 46/90 Training [5%] Loss: 0.5853 Accuracy: 72.73% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [25%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [35%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5831 Accuracy: 72.92% Training [45%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5831 Accuracy: 72.92% Training [55%] Loss: 0.5835 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5849 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [75%] Loss: 0.5835 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [85%] Loss: 0.5831 Accuracy: 72.92% Training [90%] Loss: 0.5831 Accuracy: 72.92% Training [95%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 414.4078679084778
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 414.4078679084778
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.01_optimizerAdam | 414.407868 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.1, optimizer: SGD Current: 47/90 Training [5%] Loss: 0.5836 Accuracy: 72.95% Training [10%] Loss: 0.5828 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5831 Accuracy: 72.92% Training [25%] Loss: 0.5831 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [35%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5849 Accuracy: 72.92% Training [50%] Loss: 0.5846 Accuracy: 72.92% Training [55%] Loss: 0.5835 Accuracy: 72.92% Training [60%] Loss: 0.5846 Accuracy: 72.92% Training [65%] Loss: 0.5849 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [75%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5853 Accuracy: 72.92% Training [85%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5849 Accuracy: 72.92% Time taken: 380.2517418861389
Performance on test data: Loss: 0.5753 Accuracy: 73.91%
Time taken: 380.2517418861389
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.1_optimizerSGD | 380.251742 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 0.1, optimizer: Adam Current: 48/90 Training [5%] Loss: 0.5847 Accuracy: 72.68% Training [10%] Loss: 0.5835 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5853 Accuracy: 72.92% Training [25%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [35%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [45%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5831 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [65%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5853 Accuracy: 72.92% Training [75%] Loss: 0.5831 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [85%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5831 Accuracy: 72.92% Training [95%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5846 Accuracy: 72.92% Time taken: 411.387987613678
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 411.387987613678
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr0.1_optimizerAdam | 411.387988 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 1, optimizer: SGD Current: 49/90 Training [5%] Loss: 0.5861 Accuracy: 72.92% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5831 Accuracy: 72.92% Training [25%] Loss: 0.5846 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [35%] Loss: 0.5831 Accuracy: 72.92% Training [40%] Loss: 0.5831 Accuracy: 72.92% Training [45%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5853 Accuracy: 72.92% Training [55%] Loss: 0.5849 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [65%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5831 Accuracy: 72.92% Training [75%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5831 Accuracy: 72.92% Training [85%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5846 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5849 Accuracy: 72.92% Time taken: 378.5418047904968
Performance on test data: Loss: 0.5753 Accuracy: 73.91%
Time taken: 378.5418047904968
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr1_optimizerSGD | 378.541805 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 20, learning rate: 1, optimizer: Adam Current: 50/90 Training [5%] Loss: 0.5852 Accuracy: 72.60% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [15%] Loss: 0.5846 Accuracy: 72.92% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [25%] Loss: 0.5853 Accuracy: 72.92% Training [30%] Loss: 0.5846 Accuracy: 72.92% Training [35%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5831 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5831 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [75%] Loss: 0.5849 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5831 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 419.5491225719452
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 419.5491225719452
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs20_lr1_optimizerAdam | 419.549123 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.0001, optimizer: SGD Current: 51/90 Training [3%] Loss: 0.5755 Accuracy: 72.99% Training [7%] Loss: 0.5182 Accuracy: 79.86% Training [10%] Loss: 0.4720 Accuracy: 87.07% Training [13%] Loss: 0.4549 Accuracy: 88.47% Training [17%] Loss: 0.4412 Accuracy: 89.89% Training [20%] Loss: 0.4332 Accuracy: 90.43% Training [23%] Loss: 0.4270 Accuracy: 91.10% Training [27%] Loss: 0.4230 Accuracy: 91.17% Training [30%] Loss: 0.4194 Accuracy: 91.44% Training [33%] Loss: 0.4154 Accuracy: 91.93% Training [37%] Loss: 0.4125 Accuracy: 92.03% Training [40%] Loss: 0.4092 Accuracy: 92.37% Training [43%] Loss: 0.4077 Accuracy: 92.22% Training [47%] Loss: 0.4052 Accuracy: 92.57% Training [50%] Loss: 0.4041 Accuracy: 92.67% Training [53%] Loss: 0.4035 Accuracy: 92.64% Training [57%] Loss: 0.4013 Accuracy: 92.76% Training [60%] Loss: 0.3996 Accuracy: 92.96% Training [63%] Loss: 0.3994 Accuracy: 92.81% Training [67%] Loss: 0.3984 Accuracy: 92.89% Training [70%] Loss: 0.3970 Accuracy: 93.16% Training [73%] Loss: 0.3960 Accuracy: 93.08% Training [77%] Loss: 0.3954 Accuracy: 93.23% Training [80%] Loss: 0.3939 Accuracy: 93.03% Training [83%] Loss: 0.3931 Accuracy: 93.48% Training [87%] Loss: 0.3923 Accuracy: 93.28% Training [90%] Loss: 0.3929 Accuracy: 93.18% Training [93%] Loss: 0.3917 Accuracy: 93.30% Training [97%] Loss: 0.3901 Accuracy: 93.48% Training [100%] Loss: 0.3904 Accuracy: 93.30% Time taken: 569.9762291908264
Performance on test data: Loss: 0.3829 Accuracy: 94.33%
Time taken: 569.9762291908264
Test size: 1165
Total Accuracy: 0.9433476394849786
Total Precision: 0.9563719862227325
Total Recall: 0.967479674796748
Total F1 Score: 0.9618937644341802
Classification Report:
precision recall f1-score support
0 0.90 0.88 0.89 304
1 0.96 0.97 0.96 861
accuracy 0.94 1165
macro avg 0.93 0.92 0.93 1165
weighted avg 0.94 0.94 0.94 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.0001_optimizerSGD | 569.976229 | 0.943348 | 0.956372 | 0.96748 | 0.961894 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.0001, optimizer: Adam Current: 52/90 Training [3%] Loss: 0.5853 Accuracy: 72.80% Training [7%] Loss: 0.5835 Accuracy: 72.92% Training [10%] Loss: 0.5839 Accuracy: 72.92% Training [13%] Loss: 0.5842 Accuracy: 72.92% Training [17%] Loss: 0.5853 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [23%] Loss: 0.5846 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5846 Accuracy: 72.92% Training [33%] Loss: 0.5839 Accuracy: 72.92% Training [37%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [43%] Loss: 0.5835 Accuracy: 72.92% Training [47%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [53%] Loss: 0.5828 Accuracy: 72.92% Training [57%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5849 Accuracy: 72.92% Training [70%] Loss: 0.5853 Accuracy: 72.92% Training [73%] Loss: 0.5839 Accuracy: 72.92% Training [77%] Loss: 0.5828 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [83%] Loss: 0.5842 Accuracy: 72.92% Training [87%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5846 Accuracy: 72.92% Training [93%] Loss: 0.5842 Accuracy: 72.92% Training [97%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 625.048401594162
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 625.048401594162
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.0001_optimizerAdam | 625.048402 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.001, optimizer: SGD Current: 53/90 Training [3%] Loss: 0.5846 Accuracy: 72.92% Training [7%] Loss: 0.5849 Accuracy: 72.92% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [13%] Loss: 0.5839 Accuracy: 72.92% Training [17%] Loss: 0.5846 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [23%] Loss: 0.5839 Accuracy: 72.92% Training [27%] Loss: 0.5835 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [33%] Loss: 0.5846 Accuracy: 72.92% Training [37%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5839 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5846 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [53%] Loss: 0.5849 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5849 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5839 Accuracy: 72.92% Training [77%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5835 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [93%] Loss: 0.5839 Accuracy: 72.92% Training [97%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 569.826878786087
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 569.826878786087
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.001_optimizerSGD | 569.826879 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.001, optimizer: Adam Current: 54/90 Training [3%] Loss: 0.5853 Accuracy: 72.48% Training [7%] Loss: 0.5828 Accuracy: 72.92% Training [10%] Loss: 0.5839 Accuracy: 72.92% Training [13%] Loss: 0.5842 Accuracy: 72.92% Training [17%] Loss: 0.5835 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [23%] Loss: 0.5831 Accuracy: 72.92% Training [27%] Loss: 0.5849 Accuracy: 72.92% Training [30%] Loss: 0.5831 Accuracy: 72.92% Training [33%] Loss: 0.5839 Accuracy: 72.92% Training [37%] Loss: 0.5853 Accuracy: 72.92% Training [40%] Loss: 0.5835 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [53%] Loss: 0.5835 Accuracy: 72.92% Training [57%] Loss: 0.5835 Accuracy: 72.92% Training [60%] Loss: 0.5849 Accuracy: 72.92% Training [63%] Loss: 0.5831 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5849 Accuracy: 72.92% Training [73%] Loss: 0.5842 Accuracy: 72.92% Training [77%] Loss: 0.5846 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [83%] Loss: 0.5831 Accuracy: 72.92% Training [87%] Loss: 0.5853 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5842 Accuracy: 72.92% Training [97%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5849 Accuracy: 72.92% Time taken: 620.8634331226349
Performance on test data: Loss: 0.5753 Accuracy: 73.91%
Time taken: 620.8634331226349
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.001_optimizerAdam | 620.863433 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.01, optimizer: SGD Current: 55/90 Training [3%] Loss: 0.5849 Accuracy: 72.97% Training [7%] Loss: 0.5835 Accuracy: 72.92% Training [10%] Loss: 0.5835 Accuracy: 72.92% Training [13%] Loss: 0.5853 Accuracy: 72.92% Training [17%] Loss: 0.5835 Accuracy: 72.92% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [23%] Loss: 0.5846 Accuracy: 72.92% Training [27%] Loss: 0.5831 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [33%] Loss: 0.5846 Accuracy: 72.92% Training [37%] Loss: 0.5849 Accuracy: 72.92% Training [40%] Loss: 0.5835 Accuracy: 72.92% Training [43%] Loss: 0.5839 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [53%] Loss: 0.5846 Accuracy: 72.92% Training [57%] Loss: 0.5849 Accuracy: 72.92% Training [60%] Loss: 0.5846 Accuracy: 72.92% Training [63%] Loss: 0.5835 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [73%] Loss: 0.5831 Accuracy: 72.92% Training [77%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [83%] Loss: 0.5842 Accuracy: 72.92% Training [87%] Loss: 0.5849 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [93%] Loss: 0.5839 Accuracy: 72.92% Training [97%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 570.585643529892
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 570.585643529892
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.01_optimizerSGD | 570.585644 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.01, optimizer: Adam Current: 56/90 Training [3%] Loss: 0.5857 Accuracy: 72.48% Training [7%] Loss: 0.5853 Accuracy: 72.92% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [13%] Loss: 0.5835 Accuracy: 72.92% Training [17%] Loss: 0.5853 Accuracy: 72.92% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [23%] Loss: 0.5839 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [33%] Loss: 0.5835 Accuracy: 72.92% Training [37%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [43%] Loss: 0.5839 Accuracy: 72.92% Training [47%] Loss: 0.5831 Accuracy: 72.92% Training [50%] Loss: 0.5849 Accuracy: 72.92% Training [53%] Loss: 0.5853 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [73%] Loss: 0.5835 Accuracy: 72.92% Training [77%] Loss: 0.5842 Accuracy: 72.92% Training [80%] Loss: 0.5849 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5849 Accuracy: 72.92% Training [93%] Loss: 0.5846 Accuracy: 72.92% Training [97%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 622.9756751060486
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 622.9756751060486
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.01_optimizerAdam | 622.975675 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.1, optimizer: SGD Current: 57/90 Training [3%] Loss: 0.5844 Accuracy: 72.73% Training [7%] Loss: 0.5839 Accuracy: 72.92% Training [10%] Loss: 0.5831 Accuracy: 72.92% Training [13%] Loss: 0.5846 Accuracy: 72.92% Training [17%] Loss: 0.5846 Accuracy: 72.92% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [23%] Loss: 0.5831 Accuracy: 72.92% Training [27%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [33%] Loss: 0.5849 Accuracy: 72.92% Training [37%] Loss: 0.5846 Accuracy: 72.92% Training [40%] Loss: 0.5853 Accuracy: 72.92% Training [43%] Loss: 0.5835 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [53%] Loss: 0.5849 Accuracy: 72.92% Training [57%] Loss: 0.5828 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5846 Accuracy: 72.92% Training [73%] Loss: 0.5846 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5853 Accuracy: 72.92% Training [83%] Loss: 0.5835 Accuracy: 72.92% Training [87%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [93%] Loss: 0.5839 Accuracy: 72.92% Training [97%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 567.6742696762085
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 567.6742696762085
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.1_optimizerSGD | 567.67427 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 0.1, optimizer: Adam Current: 58/90 Training [3%] Loss: 0.5847 Accuracy: 72.85% Training [7%] Loss: 0.5849 Accuracy: 72.92% Training [10%] Loss: 0.5835 Accuracy: 72.92% Training [13%] Loss: 0.5842 Accuracy: 72.92% Training [17%] Loss: 0.5846 Accuracy: 72.92% Training [20%] Loss: 0.5846 Accuracy: 72.92% Training [23%] Loss: 0.5835 Accuracy: 72.92% Training [27%] Loss: 0.5835 Accuracy: 72.92% Training [30%] Loss: 0.5831 Accuracy: 72.92% Training [33%] Loss: 0.5831 Accuracy: 72.92% Training [37%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5846 Accuracy: 72.92% Training [53%] Loss: 0.5839 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5831 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [73%] Loss: 0.5835 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [83%] Loss: 0.5842 Accuracy: 72.92% Training [87%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [93%] Loss: 0.5842 Accuracy: 72.92% Training [97%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 626.2813708782196
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 626.2813708782196
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr0.1_optimizerAdam | 626.281371 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 1, optimizer: SGD Current: 59/90 Training [3%] Loss: 0.5834 Accuracy: 72.82% Training [7%] Loss: 0.5846 Accuracy: 72.92% Training [10%] Loss: 0.5846 Accuracy: 72.92% Training [13%] Loss: 0.5835 Accuracy: 72.92% Training [17%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5853 Accuracy: 72.92% Training [23%] Loss: 0.5839 Accuracy: 72.92% Training [27%] Loss: 0.5835 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [33%] Loss: 0.5849 Accuracy: 72.92% Training [37%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [53%] Loss: 0.5842 Accuracy: 72.92% Training [57%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5856 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5831 Accuracy: 72.92% Training [73%] Loss: 0.5831 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5849 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5828 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5835 Accuracy: 72.92% Training [97%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 567.8547921180725
Performance on test data: Loss: 0.5752 Accuracy: 73.91%
Time taken: 567.8547921180725
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr1_optimizerSGD | 567.854792 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 32, epochs: 30, learning rate: 1, optimizer: Adam Current: 60/90 Training [3%] Loss: 0.5841 Accuracy: 72.92% Training [7%] Loss: 0.5842 Accuracy: 72.92% Training [10%] Loss: 0.5835 Accuracy: 72.92% Training [13%] Loss: 0.5839 Accuracy: 72.92% Training [17%] Loss: 0.5831 Accuracy: 72.92% Training [20%] Loss: 0.5835 Accuracy: 72.92% Training [23%] Loss: 0.5846 Accuracy: 72.92% Training [27%] Loss: 0.5853 Accuracy: 72.92% Training [30%] Loss: 0.5839 Accuracy: 72.92% Training [33%] Loss: 0.5839 Accuracy: 72.92% Training [37%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5846 Accuracy: 72.92% Training [43%] Loss: 0.5835 Accuracy: 72.92% Training [47%] Loss: 0.5846 Accuracy: 72.92% Training [50%] Loss: 0.5831 Accuracy: 72.92% Training [53%] Loss: 0.5835 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5839 Accuracy: 72.92% Training [67%] Loss: 0.5846 Accuracy: 72.92% Training [70%] Loss: 0.5835 Accuracy: 72.92% Training [73%] Loss: 0.5842 Accuracy: 72.92% Training [77%] Loss: 0.5846 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [83%] Loss: 0.5831 Accuracy: 72.92% Training [87%] Loss: 0.5835 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [93%] Loss: 0.5842 Accuracy: 72.92% Training [97%] Loss: 0.5853 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 638.1714425086975
Performance on test data: Loss: 0.5751 Accuracy: 73.91%
Time taken: 638.1714425086975
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch32_epochs30_lr1_optimizerAdam | 638.171443 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.0001, optimizer: SGD Current: 61/90 Training [10%] Loss: 0.5810 Accuracy: 72.68% Training [20%] Loss: 0.5511 Accuracy: 72.92% Training [30%] Loss: 0.5266 Accuracy: 72.92% Training [40%] Loss: 0.5132 Accuracy: 72.92% Training [50%] Loss: 0.5051 Accuracy: 72.97% Training [60%] Loss: 0.4986 Accuracy: 72.97% Training [70%] Loss: 0.4937 Accuracy: 73.04% Training [80%] Loss: 0.4904 Accuracy: 73.09% Training [90%] Loss: 0.4875 Accuracy: 73.17% Training [100%] Loss: 0.4850 Accuracy: 73.19% Time taken: 203.2997224330902
Performance on test data: Loss: 0.4745 Accuracy: 74.16%
Time taken: 203.2997224330902
Test size: 1165
Total Accuracy: 0.7416309012875536
Total Precision: 0.7409638554216867
Total Recall: 1.0
Total F1 Score: 0.8512110726643599
Classification Report:
precision recall f1-score support
0 1.00 0.01 0.02 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.87 0.50 0.44 1165
weighted avg 0.81 0.74 0.63 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.0001_optimizerSGD | 203.299722 | 0.741631 | 0.740964 | 1.0 | 0.851211 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.0001, optimizer: Adam Current: 62/90 Training [10%] Loss: 0.5861 Accuracy: 72.33% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5836 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [60%] Loss: 0.5846 Accuracy: 72.92% Training [70%] Loss: 0.5844 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5844 Accuracy: 72.92% Training [100%] Loss: 0.5838 Accuracy: 72.92% Time taken: 204.6422119140625
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 204.6422119140625
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.0001_optimizerAdam | 204.642212 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.001, optimizer: SGD Current: 63/90 Training [10%] Loss: 0.5851 Accuracy: 72.92% Training [20%] Loss: 0.5844 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5843 Accuracy: 72.92% Training [70%] Loss: 0.5843 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5843 Accuracy: 72.92% Time taken: 202.7689220905304
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 202.7689220905304
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.001_optimizerSGD | 202.768922 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.001, optimizer: Adam Current: 64/90 Training [10%] Loss: 0.5870 Accuracy: 72.82% Training [20%] Loss: 0.5836 Accuracy: 72.92% Training [30%] Loss: 0.5843 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5838 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5836 Accuracy: 72.92% Training [90%] Loss: 0.5837 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 203.2465853691101
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 203.2465853691101
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.001_optimizerAdam | 203.246585 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.01, optimizer: SGD Current: 65/90 Training [10%] Loss: 0.5861 Accuracy: 72.75% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5834 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5837 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5837 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5835 Accuracy: 72.92% Time taken: 199.65758228302002
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 199.65758228302002
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.01_optimizerSGD | 199.657582 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.01, optimizer: Adam Current: 66/90 Training [10%] Loss: 0.5865 Accuracy: 72.77% Training [20%] Loss: 0.5843 Accuracy: 72.92% Training [30%] Loss: 0.5843 Accuracy: 72.92% Training [40%] Loss: 0.5837 Accuracy: 72.92% Training [50%] Loss: 0.5850 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 201.059876203537
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 201.059876203537
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.01_optimizerAdam | 201.059876 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.1, optimizer: SGD Current: 67/90 Training [10%] Loss: 0.5851 Accuracy: 72.38% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5850 Accuracy: 72.92% Training [50%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5848 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5838 Accuracy: 72.92% Training [90%] Loss: 0.5844 Accuracy: 72.92% Training [100%] Loss: 0.5836 Accuracy: 72.92% Time taken: 198.21016931533813
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 198.21016931533813
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.1_optimizerSGD | 198.210169 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 0.1, optimizer: Adam Current: 68/90 Training [10%] Loss: 0.5863 Accuracy: 72.14% Training [20%] Loss: 0.5848 Accuracy: 72.92% Training [30%] Loss: 0.5837 Accuracy: 72.92% Training [40%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5844 Accuracy: 72.92% Training [70%] Loss: 0.5846 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5843 Accuracy: 72.92% Training [100%] Loss: 0.5836 Accuracy: 72.92% Time taken: 200.74035692214966
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 200.74035692214966
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr0.1_optimizerAdam | 200.740357 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 1, optimizer: SGD Current: 69/90 Training [10%] Loss: 0.5862 Accuracy: 72.92% Training [20%] Loss: 0.5843 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5836 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5838 Accuracy: 72.92% Training [70%] Loss: 0.5836 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 198.99372816085815
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 198.99372816085815
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr1_optimizerSGD | 198.993728 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 10, learning rate: 1, optimizer: Adam Current: 70/90 Training [10%] Loss: 0.5855 Accuracy: 72.92% Training [20%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5843 Accuracy: 72.92% Training [70%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5843 Accuracy: 72.92% Time taken: 201.24700164794922
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 201.24700164794922
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs10_lr1_optimizerAdam | 201.247002 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.0001, optimizer: SGD Current: 71/90 Training [5%] Loss: 0.5839 Accuracy: 72.43% Training [10%] Loss: 0.5552 Accuracy: 72.92% Training [15%] Loss: 0.5290 Accuracy: 73.12% Training [20%] Loss: 0.5107 Accuracy: 78.27% Training [25%] Loss: 0.4896 Accuracy: 85.09% Training [30%] Loss: 0.4745 Accuracy: 87.27% Training [35%] Loss: 0.4621 Accuracy: 88.55% Training [40%] Loss: 0.4535 Accuracy: 89.31% Training [45%] Loss: 0.4464 Accuracy: 89.60% Training [50%] Loss: 0.4416 Accuracy: 90.09% Training [55%] Loss: 0.4366 Accuracy: 90.46% Training [60%] Loss: 0.4323 Accuracy: 90.80% Training [65%] Loss: 0.4295 Accuracy: 90.97% Training [70%] Loss: 0.4263 Accuracy: 91.22% Training [75%] Loss: 0.4236 Accuracy: 91.42% Training [80%] Loss: 0.4214 Accuracy: 91.56% Training [85%] Loss: 0.4194 Accuracy: 91.51% Training [90%] Loss: 0.4174 Accuracy: 91.95% Training [95%] Loss: 0.4158 Accuracy: 92.03% Training [100%] Loss: 0.4146 Accuracy: 92.10% Time taken: 399.7247004508972
Performance on test data: Loss: 0.4042 Accuracy: 92.79%
Time taken: 399.7247004508972
Test size: 1165
Total Accuracy: 0.9278969957081545
Total Precision: 0.9302325581395349
Total Recall: 0.975609756097561
Total F1 Score: 0.9523809523809524
Classification Report:
precision recall f1-score support
0 0.92 0.79 0.85 304
1 0.93 0.98 0.95 861
accuracy 0.93 1165
macro avg 0.93 0.88 0.90 1165
weighted avg 0.93 0.93 0.93 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.0001_optimizerSGD | 399.7247 | 0.927897 | 0.930233 | 0.97561 | 0.952381 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.0001, optimizer: Adam Current: 72/90 Training [5%] Loss: 0.5858 Accuracy: 72.28% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [15%] Loss: 0.5843 Accuracy: 72.92% Training [20%] Loss: 0.5845 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5844 Accuracy: 72.92% Training [35%] Loss: 0.5843 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5834 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5837 Accuracy: 72.92% Training [85%] Loss: 0.5838 Accuracy: 72.92% Training [90%] Loss: 0.5845 Accuracy: 72.92% Training [95%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5837 Accuracy: 72.92% Time taken: 410.6412582397461
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 410.6412582397461
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.0001_optimizerAdam | 410.641258 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.001, optimizer: SGD Current: 73/90 Training [5%] Loss: 0.5851 Accuracy: 72.19% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [15%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5837 Accuracy: 72.92% Training [25%] Loss: 0.5844 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5838 Accuracy: 72.92% Training [40%] Loss: 0.5843 Accuracy: 72.92% Training [45%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5843 Accuracy: 72.92% Training [55%] Loss: 0.5839 Accuracy: 72.92% Training [60%] Loss: 0.5837 Accuracy: 72.92% Training [65%] Loss: 0.5835 Accuracy: 72.92% Training [70%] Loss: 0.5838 Accuracy: 72.92% Training [75%] Loss: 0.5843 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5846 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [95%] Loss: 0.5844 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 400.1477816104889
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 400.1477816104889
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.001_optimizerSGD | 400.147782 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.001, optimizer: Adam Current: 74/90 Training [5%] Loss: 0.5850 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [15%] Loss: 0.5842 Accuracy: 72.92% Training [20%] Loss: 0.5834 Accuracy: 72.92% Training [25%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [35%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5837 Accuracy: 72.92% Training [45%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [55%] Loss: 0.5838 Accuracy: 72.92% Training [60%] Loss: 0.5840 Accuracy: 72.92% Training [65%] Loss: 0.5843 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5843 Accuracy: 72.92% Training [80%] Loss: 0.5837 Accuracy: 72.92% Training [85%] Loss: 0.5840 Accuracy: 72.92% Training [90%] Loss: 0.5835 Accuracy: 72.92% Training [95%] Loss: 0.5839 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 409.8297984600067
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 409.8297984600067
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.001_optimizerAdam | 409.829798 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.01, optimizer: SGD Current: 75/90 Training [5%] Loss: 0.5876 Accuracy: 71.99% Training [10%] Loss: 0.5844 Accuracy: 72.92% Training [15%] Loss: 0.5838 Accuracy: 72.92% Training [20%] Loss: 0.5836 Accuracy: 72.92% Training [25%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [35%] Loss: 0.5839 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [45%] Loss: 0.5844 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [65%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5846 Accuracy: 72.92% Training [75%] Loss: 0.5843 Accuracy: 72.92% Training [80%] Loss: 0.5841 Accuracy: 72.92% Training [85%] Loss: 0.5844 Accuracy: 72.92% Training [90%] Loss: 0.5845 Accuracy: 72.92% Training [95%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 400.8694086074829
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 400.8694086074829
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.01_optimizerSGD | 400.869409 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.01, optimizer: Adam Current: 76/90 Training [5%] Loss: 0.5866 Accuracy: 72.95% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [15%] Loss: 0.5836 Accuracy: 72.92% Training [20%] Loss: 0.5837 Accuracy: 72.92% Training [25%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5838 Accuracy: 72.92% Training [35%] Loss: 0.5848 Accuracy: 72.92% Training [40%] Loss: 0.5836 Accuracy: 72.92% Training [45%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [65%] Loss: 0.5836 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [75%] Loss: 0.5846 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5844 Accuracy: 72.92% Training [95%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 410.1120808124542
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 410.1120808124542
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.01_optimizerAdam | 410.112081 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.1, optimizer: SGD Current: 77/90 Training [5%] Loss: 0.5858 Accuracy: 72.55% Training [10%] Loss: 0.5836 Accuracy: 72.92% Training [15%] Loss: 0.5843 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [35%] Loss: 0.5835 Accuracy: 72.92% Training [40%] Loss: 0.5838 Accuracy: 72.92% Training [45%] Loss: 0.5843 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [55%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5837 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5844 Accuracy: 72.92% Training [85%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [95%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5842 Accuracy: 72.92% Time taken: 398.9622218608856
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 398.9622218608856
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.1_optimizerSGD | 398.962222 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 0.1, optimizer: Adam Current: 78/90 Training [5%] Loss: 0.5863 Accuracy: 72.19% Training [10%] Loss: 0.5832 Accuracy: 72.92% Training [15%] Loss: 0.5837 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5836 Accuracy: 72.92% Training [30%] Loss: 0.5837 Accuracy: 72.92% Training [35%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5838 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [75%] Loss: 0.5834 Accuracy: 72.92% Training [80%] Loss: 0.5845 Accuracy: 72.92% Training [85%] Loss: 0.5836 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [95%] Loss: 0.5844 Accuracy: 72.92% Training [100%] Loss: 0.5848 Accuracy: 72.92% Time taken: 409.20268964767456
Performance on test data: Loss: 0.5748 Accuracy: 73.91%
Time taken: 409.20268964767456
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr0.1_optimizerAdam | 409.20269 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 1, optimizer: SGD Current: 79/90 Training [5%] Loss: 0.5860 Accuracy: 72.31% Training [10%] Loss: 0.5834 Accuracy: 72.92% Training [15%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5840 Accuracy: 72.92% Training [25%] Loss: 0.5840 Accuracy: 72.92% Training [30%] Loss: 0.5841 Accuracy: 72.92% Training [35%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5842 Accuracy: 72.92% Training [45%] Loss: 0.5841 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5835 Accuracy: 72.92% Training [65%] Loss: 0.5836 Accuracy: 72.92% Training [70%] Loss: 0.5841 Accuracy: 72.92% Training [75%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [85%] Loss: 0.5843 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [95%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5838 Accuracy: 72.92% Time taken: 400.8607876300812
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 400.8607876300812
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr1_optimizerSGD | 400.860788 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 20, learning rate: 1, optimizer: Adam Current: 80/90 Training [5%] Loss: 0.5864 Accuracy: 72.65% Training [10%] Loss: 0.5837 Accuracy: 72.92% Training [15%] Loss: 0.5838 Accuracy: 72.92% Training [20%] Loss: 0.5836 Accuracy: 72.92% Training [25%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5838 Accuracy: 72.92% Training [35%] Loss: 0.5837 Accuracy: 72.92% Training [40%] Loss: 0.5839 Accuracy: 72.92% Training [45%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [55%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5842 Accuracy: 72.92% Training [65%] Loss: 0.5837 Accuracy: 72.92% Training [70%] Loss: 0.5837 Accuracy: 72.92% Training [75%] Loss: 0.5845 Accuracy: 72.92% Training [80%] Loss: 0.5837 Accuracy: 72.92% Training [85%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5840 Accuracy: 72.92% Training [95%] Loss: 0.5843 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 410.8679690361023
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 410.8679690361023
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs20_lr1_optimizerAdam | 410.867969 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.0001, optimizer: SGD Current: 81/90 Training [3%] Loss: 0.5871 Accuracy: 72.92% Training [7%] Loss: 0.5651 Accuracy: 72.92% Training [10%] Loss: 0.5348 Accuracy: 72.92% Training [13%] Loss: 0.5174 Accuracy: 72.99% Training [17%] Loss: 0.5075 Accuracy: 73.39% Training [20%] Loss: 0.4992 Accuracy: 76.77% Training [23%] Loss: 0.4866 Accuracy: 85.14% Training [27%] Loss: 0.4721 Accuracy: 87.54% Training [30%] Loss: 0.4601 Accuracy: 88.96% Training [33%] Loss: 0.4518 Accuracy: 89.67% Training [37%] Loss: 0.4444 Accuracy: 90.19% Training [40%] Loss: 0.4401 Accuracy: 90.36% Training [43%] Loss: 0.4346 Accuracy: 90.63% Training [47%] Loss: 0.4303 Accuracy: 90.78% Training [50%] Loss: 0.4273 Accuracy: 91.37% Training [53%] Loss: 0.4250 Accuracy: 91.02% Training [57%] Loss: 0.4230 Accuracy: 91.44% Training [60%] Loss: 0.4200 Accuracy: 91.83% Training [63%] Loss: 0.4183 Accuracy: 91.91% Training [67%] Loss: 0.4166 Accuracy: 92.05% Training [70%] Loss: 0.4150 Accuracy: 91.88% Training [73%] Loss: 0.4135 Accuracy: 92.40% Training [77%] Loss: 0.4117 Accuracy: 92.30% Training [80%] Loss: 0.4112 Accuracy: 92.35% Training [83%] Loss: 0.4094 Accuracy: 92.45% Training [87%] Loss: 0.4087 Accuracy: 92.54% Training [90%] Loss: 0.4074 Accuracy: 92.67% Training [93%] Loss: 0.4062 Accuracy: 92.62% Training [97%] Loss: 0.4055 Accuracy: 92.72% Training [100%] Loss: 0.4046 Accuracy: 92.67% Time taken: 594.5287621021271
Performance on test data: Loss: 0.3980 Accuracy: 93.65%
Time taken: 594.5287621021271
Test size: 1165
Total Accuracy: 0.936480686695279
Total Precision: 0.9591598599766628
Total Recall: 0.9547038327526133
Total F1 Score: 0.9569266589057043
Classification Report:
precision recall f1-score support
0 0.87 0.88 0.88 304
1 0.96 0.95 0.96 861
accuracy 0.94 1165
macro avg 0.92 0.92 0.92 1165
weighted avg 0.94 0.94 0.94 1165
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.0001_optimizerSGD | 594.528762 | 0.936481 | 0.95916 | 0.954704 | 0.956927 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.0001, optimizer: Adam Current: 82/90 Training [3%] Loss: 0.5857 Accuracy: 72.70% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5837 Accuracy: 72.92% Training [13%] Loss: 0.5840 Accuracy: 72.92% Training [17%] Loss: 0.5843 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5838 Accuracy: 72.92% Training [27%] Loss: 0.5835 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5844 Accuracy: 72.92% Training [37%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5837 Accuracy: 72.92% Training [47%] Loss: 0.5840 Accuracy: 72.92% Training [50%] Loss: 0.5841 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5846 Accuracy: 72.92% Training [60%] Loss: 0.5836 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5842 Accuracy: 72.92% Training [77%] Loss: 0.5834 Accuracy: 72.92% Training [80%] Loss: 0.5837 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5849 Accuracy: 72.92% Training [90%] Loss: 0.5836 Accuracy: 72.92% Training [93%] Loss: 0.5838 Accuracy: 72.92% Training [97%] Loss: 0.5838 Accuracy: 72.92% Training [100%] Loss: 0.5839 Accuracy: 72.92% Time taken: 619.6276483535767
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 619.6276483535767
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.0001_optimizerAdam | 619.627648 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.001, optimizer: SGD Current: 83/90 Training [3%] Loss: 0.5862 Accuracy: 72.92% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [13%] Loss: 0.5838 Accuracy: 72.92% Training [17%] Loss: 0.5841 Accuracy: 72.92% Training [20%] Loss: 0.5836 Accuracy: 72.92% Training [23%] Loss: 0.5835 Accuracy: 72.92% Training [27%] Loss: 0.5838 Accuracy: 72.92% Training [30%] Loss: 0.5837 Accuracy: 72.92% Training [33%] Loss: 0.5838 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5837 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5839 Accuracy: 72.92% Training [53%] Loss: 0.5840 Accuracy: 72.92% Training [57%] Loss: 0.5837 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5836 Accuracy: 72.92% Training [67%] Loss: 0.5843 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5841 Accuracy: 72.92% Training [77%] Loss: 0.5837 Accuracy: 72.92% Training [80%] Loss: 0.5845 Accuracy: 72.92% Training [83%] Loss: 0.5841 Accuracy: 72.92% Training [87%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5844 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5844 Accuracy: 72.92% Time taken: 599.9544579982758
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 599.9544579982758
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.001_optimizerSGD | 599.954458 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.001, optimizer: Adam Current: 84/90 Training [3%] Loss: 0.5860 Accuracy: 72.19% Training [7%] Loss: 0.5840 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [13%] Loss: 0.5833 Accuracy: 72.92% Training [17%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5843 Accuracy: 72.92% Training [23%] Loss: 0.5838 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5841 Accuracy: 72.92% Training [37%] Loss: 0.5844 Accuracy: 72.92% Training [40%] Loss: 0.5840 Accuracy: 72.92% Training [43%] Loss: 0.5846 Accuracy: 72.92% Training [47%] Loss: 0.5835 Accuracy: 72.92% Training [50%] Loss: 0.5840 Accuracy: 72.92% Training [53%] Loss: 0.5842 Accuracy: 72.92% Training [57%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5837 Accuracy: 72.92% Training [63%] Loss: 0.5845 Accuracy: 72.92% Training [67%] Loss: 0.5846 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5839 Accuracy: 72.92% Training [77%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5842 Accuracy: 72.92% Training [83%] Loss: 0.5839 Accuracy: 72.92% Training [87%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5843 Accuracy: 72.92% Training [93%] Loss: 0.5838 Accuracy: 72.92% Training [97%] Loss: 0.5840 Accuracy: 72.92% Training [100%] Loss: 0.5837 Accuracy: 72.92% Time taken: 623.4417991638184
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 623.4417991638184
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.001_optimizerAdam | 623.441799 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.01, optimizer: SGD Current: 85/90 Training [3%] Loss: 0.5858 Accuracy: 72.31% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [13%] Loss: 0.5843 Accuracy: 72.92% Training [17%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5843 Accuracy: 72.92% Training [23%] Loss: 0.5837 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5840 Accuracy: 72.92% Training [33%] Loss: 0.5839 Accuracy: 72.92% Training [37%] Loss: 0.5838 Accuracy: 72.92% Training [40%] Loss: 0.5837 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [53%] Loss: 0.5837 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5844 Accuracy: 72.92% Training [63%] Loss: 0.5842 Accuracy: 72.92% Training [67%] Loss: 0.5842 Accuracy: 72.92% Training [70%] Loss: 0.5844 Accuracy: 72.92% Training [73%] Loss: 0.5837 Accuracy: 72.92% Training [77%] Loss: 0.5837 Accuracy: 72.92% Training [80%] Loss: 0.5844 Accuracy: 72.92% Training [83%] Loss: 0.5844 Accuracy: 72.92% Training [87%] Loss: 0.5836 Accuracy: 72.92% Training [90%] Loss: 0.5838 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5841 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 602.9936096668243
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 602.9936096668243
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.01_optimizerSGD | 602.99361 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.01, optimizer: Adam Current: 86/90 Training [3%] Loss: 0.5850 Accuracy: 72.92% Training [7%] Loss: 0.5844 Accuracy: 72.92% Training [10%] Loss: 0.5839 Accuracy: 72.92% Training [13%] Loss: 0.5843 Accuracy: 72.92% Training [17%] Loss: 0.5843 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5841 Accuracy: 72.92% Training [27%] Loss: 0.5839 Accuracy: 72.92% Training [30%] Loss: 0.5844 Accuracy: 72.92% Training [33%] Loss: 0.5842 Accuracy: 72.92% Training [37%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5837 Accuracy: 72.92% Training [43%] Loss: 0.5844 Accuracy: 72.92% Training [47%] Loss: 0.5837 Accuracy: 72.92% Training [50%] Loss: 0.5835 Accuracy: 72.92% Training [53%] Loss: 0.5831 Accuracy: 72.92% Training [57%] Loss: 0.5836 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5836 Accuracy: 72.92% Training [67%] Loss: 0.5841 Accuracy: 72.92% Training [70%] Loss: 0.5842 Accuracy: 72.92% Training [73%] Loss: 0.5845 Accuracy: 72.92% Training [77%] Loss: 0.5840 Accuracy: 72.92% Training [80%] Loss: 0.5839 Accuracy: 72.92% Training [83%] Loss: 0.5837 Accuracy: 72.92% Training [87%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5843 Accuracy: 72.92% Training [93%] Loss: 0.5842 Accuracy: 72.92% Training [97%] Loss: 0.5846 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 618.9918177127838
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 618.9918177127838
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.01_optimizerAdam | 618.991818 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.1, optimizer: SGD Current: 87/90 Training [3%] Loss: 0.5869 Accuracy: 72.14% Training [7%] Loss: 0.5843 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [13%] Loss: 0.5836 Accuracy: 72.92% Training [17%] Loss: 0.5839 Accuracy: 72.92% Training [20%] Loss: 0.5837 Accuracy: 72.92% Training [23%] Loss: 0.5840 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [33%] Loss: 0.5844 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5841 Accuracy: 72.92% Training [43%] Loss: 0.5836 Accuracy: 72.92% Training [47%] Loss: 0.5842 Accuracy: 72.92% Training [50%] Loss: 0.5843 Accuracy: 72.92% Training [53%] Loss: 0.5844 Accuracy: 72.92% Training [57%] Loss: 0.5845 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5841 Accuracy: 72.92% Training [67%] Loss: 0.5839 Accuracy: 72.92% Training [70%] Loss: 0.5838 Accuracy: 72.92% Training [73%] Loss: 0.5843 Accuracy: 72.92% Training [77%] Loss: 0.5839 Accuracy: 72.92% Training [80%] Loss: 0.5840 Accuracy: 72.92% Training [83%] Loss: 0.5843 Accuracy: 72.92% Training [87%] Loss: 0.5844 Accuracy: 72.92% Training [90%] Loss: 0.5837 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5837 Accuracy: 72.92% Training [100%] Loss: 0.5834 Accuracy: 72.92% Time taken: 601.7377729415894
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 601.7377729415894
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.1_optimizerSGD | 601.737773 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 0.1, optimizer: Adam Current: 88/90 Training [3%] Loss: 0.5865 Accuracy: 72.28% Training [7%] Loss: 0.5844 Accuracy: 72.92% Training [10%] Loss: 0.5846 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5840 Accuracy: 72.92% Training [20%] Loss: 0.5837 Accuracy: 72.92% Training [23%] Loss: 0.5843 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5843 Accuracy: 72.92% Training [33%] Loss: 0.5848 Accuracy: 72.92% Training [37%] Loss: 0.5842 Accuracy: 72.92% Training [40%] Loss: 0.5848 Accuracy: 72.92% Training [43%] Loss: 0.5844 Accuracy: 72.92% Training [47%] Loss: 0.5839 Accuracy: 72.92% Training [50%] Loss: 0.5837 Accuracy: 72.92% Training [53%] Loss: 0.5851 Accuracy: 72.92% Training [57%] Loss: 0.5843 Accuracy: 72.92% Training [60%] Loss: 0.5839 Accuracy: 72.92% Training [63%] Loss: 0.5848 Accuracy: 72.92% Training [67%] Loss: 0.5844 Accuracy: 72.92% Training [70%] Loss: 0.5843 Accuracy: 72.92% Training [73%] Loss: 0.5842 Accuracy: 72.92% Training [77%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5844 Accuracy: 72.92% Training [83%] Loss: 0.5835 Accuracy: 72.92% Training [87%] Loss: 0.5842 Accuracy: 72.92% Training [90%] Loss: 0.5842 Accuracy: 72.92% Training [93%] Loss: 0.5834 Accuracy: 72.92% Training [97%] Loss: 0.5837 Accuracy: 72.92% Training [100%] Loss: 0.5841 Accuracy: 72.92% Time taken: 623.172290802002
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 623.172290802002
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr0.1_optimizerAdam | 623.172291 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 1, optimizer: SGD Current: 89/90 Training [3%] Loss: 0.5855 Accuracy: 72.80% Training [7%] Loss: 0.5842 Accuracy: 72.92% Training [10%] Loss: 0.5842 Accuracy: 72.92% Training [13%] Loss: 0.5845 Accuracy: 72.92% Training [17%] Loss: 0.5843 Accuracy: 72.92% Training [20%] Loss: 0.5839 Accuracy: 72.92% Training [23%] Loss: 0.5842 Accuracy: 72.92% Training [27%] Loss: 0.5841 Accuracy: 72.92% Training [30%] Loss: 0.5842 Accuracy: 72.92% Training [33%] Loss: 0.5840 Accuracy: 72.92% Training [37%] Loss: 0.5840 Accuracy: 72.92% Training [40%] Loss: 0.5844 Accuracy: 72.92% Training [43%] Loss: 0.5842 Accuracy: 72.92% Training [47%] Loss: 0.5838 Accuracy: 72.92% Training [50%] Loss: 0.5844 Accuracy: 72.92% Training [53%] Loss: 0.5837 Accuracy: 72.92% Training [57%] Loss: 0.5841 Accuracy: 72.92% Training [60%] Loss: 0.5838 Accuracy: 72.92% Training [63%] Loss: 0.5839 Accuracy: 72.92% Training [67%] Loss: 0.5836 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [73%] Loss: 0.5839 Accuracy: 72.92% Training [77%] Loss: 0.5841 Accuracy: 72.92% Training [80%] Loss: 0.5838 Accuracy: 72.92% Training [83%] Loss: 0.5844 Accuracy: 72.92% Training [87%] Loss: 0.5841 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [93%] Loss: 0.5835 Accuracy: 72.92% Training [97%] Loss: 0.5842 Accuracy: 72.92% Training [100%] Loss: 0.5838 Accuracy: 72.92% Time taken: 599.8099925518036
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 599.8099925518036
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr1_optimizerSGD | 599.809993 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully Training with batch size: 64, epochs: 30, learning rate: 1, optimizer: Adam Current: 90/90 Training [3%] Loss: 0.5868 Accuracy: 72.16% Training [7%] Loss: 0.5841 Accuracy: 72.92% Training [10%] Loss: 0.5843 Accuracy: 72.92% Training [13%] Loss: 0.5841 Accuracy: 72.92% Training [17%] Loss: 0.5835 Accuracy: 72.92% Training [20%] Loss: 0.5841 Accuracy: 72.92% Training [23%] Loss: 0.5842 Accuracy: 72.92% Training [27%] Loss: 0.5842 Accuracy: 72.92% Training [30%] Loss: 0.5837 Accuracy: 72.92% Training [33%] Loss: 0.5845 Accuracy: 72.92% Training [37%] Loss: 0.5841 Accuracy: 72.92% Training [40%] Loss: 0.5837 Accuracy: 72.92% Training [43%] Loss: 0.5841 Accuracy: 72.92% Training [47%] Loss: 0.5843 Accuracy: 72.92% Training [50%] Loss: 0.5842 Accuracy: 72.92% Training [53%] Loss: 0.5841 Accuracy: 72.92% Training [57%] Loss: 0.5842 Accuracy: 72.92% Training [60%] Loss: 0.5841 Accuracy: 72.92% Training [63%] Loss: 0.5840 Accuracy: 72.92% Training [67%] Loss: 0.5845 Accuracy: 72.92% Training [70%] Loss: 0.5840 Accuracy: 72.92% Training [73%] Loss: 0.5840 Accuracy: 72.92% Training [77%] Loss: 0.5845 Accuracy: 72.92% Training [80%] Loss: 0.5844 Accuracy: 72.92% Training [83%] Loss: 0.5838 Accuracy: 72.92% Training [87%] Loss: 0.5839 Accuracy: 72.92% Training [90%] Loss: 0.5841 Accuracy: 72.92% Training [93%] Loss: 0.5841 Accuracy: 72.92% Training [97%] Loss: 0.5835 Accuracy: 72.92% Training [100%] Loss: 0.5840 Accuracy: 72.92% Time taken: 623.4913277626038
Performance on test data: Loss: 0.5747 Accuracy: 73.91%
Time taken: 623.4913277626038
Test size: 1165
Total Accuracy: 0.7390557939914163
Total Precision: 0.7390557939914163
Total Recall: 1.0
Total F1 Score: 0.8499506416584404
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 304
1 0.74 1.00 0.85 861
accuracy 0.74 1165
macro avg 0.37 0.50 0.42 1165
weighted avg 0.55 0.74 0.63 1165
c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) c:\Users\rjuya\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
| Time | Accuracy | Precision | Recall | F1 score | |
|---|---|---|---|---|---|
| Logistic Regression model_batch64_epochs30_lr1_optimizerAdam | 623.491328 | 0.739056 | 0.739056 | 1.0 | 0.849951 |
Data Stored Successfully